This document describes methods and systems for determining the drivers of consumer preferences for items, such as beverages and other consumable items.
Currently, many systems are available to recommend products to consumers. Such systems may include a consumer profile that includes data representing items that the consumer previously purchased and/or rated. While these systems use past purchase data or ratings to identify other items that the consumer may enjoy, the recommended items generally bear a direct relationship to the consumer's previously purchased or rated items. For example, in the context of wine, a system may learn that a consumer enjoyed a particular California Pinot Noir. If so, it may recommend other California Pinot Noirs, or other wines from the same vineyard as the rated wine, to the consumer.
However, current systems lack the ability to truly assess whether the user is likely to enjoy the recommended item. Nor do such systems have a desired level of ability to find and recommend apparently unrelated items that the consumer is likely to enjoy.
This document describes methods and systems directed to solving at least some of the problems described above, and/or additional problems.